Dietary Calcium Intake and the Risk of Metabolic Syndrome: A Systematic Review and Meta-Analysis

Han, Dan; Fang, Xuexian; Su, Danting; Huang, Li-Chun; He, Mengjie; Zhao, Dong; Zou, Yan

doi:10.1038/s41598-019-55507-x

Cited by 31 publications

(18 citation statements)

References 38 publications

(45 reference statements)

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“…Mg 2+ is a natural calcium (Ca 2+ ) antagonist, and its metabolic effect needs to be discussed according to Ca 2+ concentration. A recent meta-analysis suggests that high Ca 2+ dietary intake reduces the risk of MetS [ 75 ]. Other experimental data suggest that a higher Ca 2+ /Mg 2+ intracellular ratio, induced by a diet high in Ca 2+ and low in Mg 2+ , may lead to hypertension, insulin resistance, and MetS [ 76 ].…”

Section: Mg 2+ In Metabolic Syndromementioning

confidence: 99%

Magnesium in Obesity, Metabolic Syndrome, and Type 2 Diabetes

et al. 2021

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Magnesium (Mg2+) deficiency is probably the most underestimated electrolyte imbalance in Western countries. It is frequent in obese patients, subjects with type-2 diabetes and metabolic syndrome, both in adulthood and in childhood. This narrative review aims to offer insights into the pathophysiological mechanisms linking Mg2+ deficiency with obesity and the risk of developing metabolic syndrome and type 2 diabetes. Literature highlights critical issues about the treatment of Mg2+ deficiency, such as the lack of a clear definition of Mg2+ nutritional status, the use of different Mg2+ salts and dosage and the different duration of the Mg2+ supplementation. Despite the lack of agreement, an appropriate dietary pattern, including the right intake of Mg2+, improves metabolic syndrome by reducing blood pressure, hyperglycemia, and hypertriglyceridemia. This occurs through the modulation of gene expression and proteomic profile as well as through a positive influence on the composition of the intestinal microbiota and the metabolism of vitamins B1 and D.

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Section: Mg 2+ In Metabolic Syndromementioning

confidence: 99%

Magnesium in Obesity, Metabolic Syndrome, and Type 2 Diabetes

et al. 2021

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“…Among the 2313 women, 251 (10.9%) developed metabolic syndrome within 3 years of enrollment, 1240 (53.6%) did not, and the remaining 822 (35.5%) had missing outcomes. Based on previous literature 53,50,49,54 on risk factors for metabolic syndrome, we selected 60 candidate predictors (29 continuous variables and 31 discrete variables), including demographics, daily life behavior, dietary habits, sleep habits, medications, mental status, menopausal status and related factors, physical measurement, blood measurement, and bone mineral density. A list of 60 variable names and their definitions are displayed in Supplemental Table 2.…”

Section: Case Study: the Study Of Women's Health Across The Nationmentioning

confidence: 99%

Variable selection with missing data in both covariates and outcomes: Imputation and machine learning

Lin

2021

Stat Methods Med Res

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Variable selection in the presence of both missing covariates and outcomes is an important statistical research topic. Parametric regression are susceptible to misspecification, and as a result are sub-optimal for variable selection. Flexible machine learning methods mitigate the reliance on the parametric assumptions, but do not provide as naturally defined variable importance measure as the covariate effect native to parametric models. We investigate a general variable selection approach when both the covariates and outcomes can be missing at random and have general missing data patterns. This approach exploits the flexibility of machine learning models and bootstrap imputation, which is amenable to nonparametric methods in which the covariate effects are not directly available. We conduct expansive simulations investigating the practical operating characteristics of the proposed variable selection approach, when combined with four tree-based machine learning methods, extreme gradient boosting, random forests, Bayesian additive regression trees, and conditional random forests, and two commonly used parametric methods, lasso and backward stepwise selection. Numeric results suggest that, extreme gradient boosting and Bayesian additive regression trees have the overall best variable selection performance with respect to the [Formula: see text] score and Type I error, while the lasso and backward stepwise selection have subpar performance across various settings. There is no significant difference in the variable selection performance due to imputation methods. We further demonstrate the methods via a case study of risk factors for 3-year incidence of metabolic syndrome with data from the Study of Women’s Health Across the Nation.

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“…Calcium deficiency increases insulin resistance and visceral fat mass by down-regulating genes related to fat oxidation in estrogen-deficient animals [ 36 ], and it indicates the calcium deficiency is related to MetS risk. A meta-analysis has demonstrated that dietary calcium intakes are inversely related to MetS risk while a 300 mg increment of daily calcium intake can decrease the MetS risk by 7% (relative risk: 0.93; 95% CI: 0.87–0.99) [ 37 ]. Calcium and vitamin D interaction is also involved in MetS risk [ 38 , 39 ].…”

Section: Discussionmentioning

confidence: 99%

A Healthy Diet Rich in Calcium and Vitamin C Is Inversely Associated with Metabolic Syndrome Risk in Korean Adults from the KNHANES 2013–2017

et al. 2021

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The association between metabolic syndrome and eating patterns remains unclear. We hypothesized that Korean Healthy Eating Index (KHEI) scores were related to metabolic syndrome (MetS) risk in adults in a gender-dependent manner. We aimed to examine the hypothesis using the Korea National Health and Nutrition Examination Survey-VI (2013–2017) data with a complex sample survey design. Adjusted means and 95% confidence intervals of KHEI scores and nutrient intake estimated by the 24-h recall were calculated according to MetS status after adjusting for age, residence area, region, education, obesity, income, drinking status, smoking status, marriage, and exercise. Adjusted odds ratios for MetS were measured according to KHEI quartiles using logistic regression analysis while controlling for covariates. MetS incidence was significantly higher in females than in males. Those who were older, less educated, earning less income, more obese, living in rural areas, drinking severely, non-exercising, and married had higher MetS incidence than those with the opposite state. Total KHEI scores of all components KHEI scores were lower for those with MetS (MetS group) than those without MetS (Non-MetS group) in both genders. For KHEI components, having breakfast and milk and fat intake had lower scores for the MetS group than for the Non-MetS group in women, whereas fruits and milk and milk product intake had lower scores for the MetS group in men. Nutrient intake influenced the MetS risk in females more than in males. Fat, calcium, and vitamin C intakes from 24-h recall were lower in the MetS group than in the Non-MetS group in women. KHEI scores had an inverse association with MetS risk by 0.98-fold in both genders after adjusting for covariates. In conclusion, a healthy diet that includes adequate calcium and vitamin C is associated with a lower the risk of MetS in both men and women.

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Dietary Calcium Intake and the Risk of Metabolic Syndrome: A Systematic Review and Meta-Analysis

Cited by 31 publications

References 38 publications

Magnesium in Obesity, Metabolic Syndrome, and Type 2 Diabetes

Magnesium in Obesity, Metabolic Syndrome, and Type 2 Diabetes

Variable selection with missing data in both covariates and outcomes: Imputation and machine learning

A Healthy Diet Rich in Calcium and Vitamin C Is Inversely Associated with Metabolic Syndrome Risk in Korean Adults from the KNHANES 2013–2017

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